from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase__  =					transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 				lowerCAmelCase__       (			a__ )      ->List[str]:




  '''simple docstring'''

  if isinstance(a__    ,     torch.Tensor ):
    return image
  elif isinstance(a__    ,     PIL.Image.Image ):
    _UpperCamelCase           =    [image]

  _UpperCamelCase           =    [trans(img.convert("RGB" ) ) for img in image]
  _UpperCamelCase           =    torch.stack(a__ )
  return image







class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   Union[str, Any]							, lowercase_				:   Any)						->	Any:


   """simple docstring"""







   super().__init__()

   # make sure scheduler can always be converted to DDIM
   _UpperCamelCase           =    DDIMScheduler.from_config(scheduler.config)

   self.register_modules(unet=lowercase_							, scheduler=lowercase_)







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   Optional[int])						->	Any:


   """simple docstring"""







   if strength < 0 or strength > 1:
     raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Dict							, lowercase_				:   Optional[Any]							, lowercase_				:   List[Any])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    min(int(num_inference_steps * strength)							, lowercase_)

   _UpperCamelCase           =    max(num_inference_steps - init_timestep							, 0)
   _UpperCamelCase           =    self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   Dict							, lowercase_				:   Union[str, Any]							, lowercase_				:   int							, lowercase_				:   int							, lowercase_				:   Union[str, Any]=None)						->	Optional[Any]:


   """simple docstring"""







   if not isinstance(lowercase_							, (torch.Tensor, PIL.Image.Image, list)):
     raise ValueError(
         f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_)}')

   _UpperCamelCase           =    image.to(device=lowercase_							, dtype=lowercase_)

   if isinstance(lowercase_							, lowercase_) and len(lowercase_) != batch_size:
     raise ValueError(
         f'You have passed a list of generators of length {len(lowercase_)}, but requested an effective batch'
         f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

   _UpperCamelCase           =    init_latents.shape
   _UpperCamelCase           =    randn_tensor(lowercase_							, generator=lowercase_							, device=lowercase_							, dtype=lowercase_)

   # get latents
   print("add noise to latents at timestep"							, lowercase_)
   _UpperCamelCase           =    self.scheduler.add_noise(lowercase_							, lowercase_							, lowercase_)
   _UpperCamelCase           =    init_latents

   return latents







 @torch.no_grad()
 def __call__(		self				:   int							, lowercase_				:   Union[torch.FloatTensor, PIL.Image.Image] = None							, lowercase_				:   float = 0.8							, lowercase_				:   int = 1							, lowercase_				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							, lowercase_				:   float = 0.0							, lowercase_				:   int = 50							, lowercase_				:   Optional[bool] = None							, lowercase_				:   Optional[str] = "pil"							, lowercase_				:   bool = True							, )						->	Union[ImagePipelineOutput, Tuple]:


   """simple docstring"""







   self.check_inputs(lowercase_)

   # 2. Preprocess image
   _UpperCamelCase           =    preprocess(lowercase_)

   # 3. set timesteps
   self.scheduler.set_timesteps(lowercase_							, device=self.device)
   _UpperCamelCase      ,						_UpperCamelCase           =    self.get_timesteps(lowercase_							, lowercase_							, self.device)
   _UpperCamelCase           =    timesteps[:1].repeat(lowercase_)

   # 4. Prepare latent variables
   _UpperCamelCase           =    self.prepare_latents(lowercase_							, lowercase_							, lowercase_							, self.unet.dtype							, self.device							, lowercase_)
   _UpperCamelCase           =    latents

   # 5. Denoising loop
   for t in self.progress_bar(lowercase_):
     # 1. predict noise model_output
     _UpperCamelCase           =    self.unet(lowercase_							, lowercase_).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     _UpperCamelCase           =    self.scheduler.step(
         lowercase_							, lowercase_							, lowercase_							, eta=lowercase_							, use_clipped_model_output=lowercase_							, generator=lowercase_							, ).prev_sample

   _UpperCamelCase           =    (image / 2 + 0.5).clamp(0							, 1)
   _UpperCamelCase           =    image.cpu().permute(0							, 2							, 3							, 1).numpy()
   if output_type == "pil":
     _UpperCamelCase           =    self.numpy_to_pil(lowercase_)

   if not return_dict:
     return (image, latent_timestep.item())

   return ImagePipelineOutput(images=lowercase_)





# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    multiprocessing.Manager()
  _UpperCamelCase           =    manager.list()

  _UpperCamelCase           =    multiprocessing.Process(target=a__    ,     args=(check_program, result, timeout) )
  p.start()
  p.join(timeout=timeout + 1 )
  if p.is_alive():
    p.kill()

  if not result:
    result.append("timed out" )

  return {
      "task_id": task_id,
      "passed": result[0] == "passed",
      "result": result[0],
      "completion_id": completion_id,
  }


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

  with create_tempdir():
    # These system calls are needed when cleaning up tempdir.
    import os
    import shutil

    _UpperCamelCase           =    shutil.rmtree
    _UpperCamelCase           =    os.rmdir
    _UpperCamelCase           =    os.chdir

    # Disable functionalities that can make destructive changes to the test.
    reliability_guard()

    # Run program.
    try:
      _UpperCamelCase           =    {}
      with swallow_io():
        with time_limit(a__ ):
          exec(a__    ,     a__ )
      result.append("passed" )
    except TimeoutException:
      result.append("timed out" )
    except BaseException as e:
      result.append(f'failed: {e}' )

    # Needed for cleaning up.
    _UpperCamelCase           =    rmtree
    _UpperCamelCase           =    rmdir
    _UpperCamelCase           =    chdir


@contextlib.contextmanager
def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  def signal_handler(a__    ,     a__ ):
    raise TimeoutException("Timed out!" )

  signal.setitimer(signal.ITIMER_REAL    ,     a__ )
  signal.signal(signal.SIGALRM    ,     a__ )
  try:
    yield
  finally:
    signal.setitimer(signal.ITIMER_REAL    ,     0 )


@contextlib.contextmanager
def 				lowerCAmelCase__       (			)      ->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase           =    WriteOnlyStringIO()
  with contextlib.redirect_stdout(a__ ):
    with contextlib.redirect_stderr(a__ ):
      with redirect_stdin(a__ ):
        yield


@contextlib.contextmanager
def 				lowerCAmelCase__       (			)      ->Optional[int]:




  '''simple docstring'''

  with tempfile.TemporaryDirectory() as dirname:
    with chdir(a__ ):
      yield dirname




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 pass




class 					_UpperCAmelCase			(     io.StringIO  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[str]							, *lowercase_				:   str							, **lowercase_				:   List[str])						->	Optional[Any]:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   Any							, *lowercase_				:   List[Any]							, **lowercase_				:   Optional[int])						->	Optional[int]:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   int							, *lowercase_				:   int							, **lowercase_				:   str)						->	int:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   List[Any]							, *lowercase_				:   Tuple							, **lowercase_				:   Tuple)						->	Tuple:


   """simple docstring"""







   return False




class 					_UpperCAmelCase			(     contextlib._RedirectStream  ):  # type: ignore



 '''simple docstring'''




 __A			       =					'''stdin'''


@contextlib.contextmanager
def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  if root == ".":
    yield
    return
  _UpperCamelCase           =    os.getcwd()
  os.chdir(a__ )
  try:
    yield
  except BaseException as exc:
    raise exc
  finally:
    os.chdir(a__ )







def 				lowerCAmelCase__       (			a__=None )      ->Optional[int]:




  '''simple docstring'''

  if maximum_memory_bytes is not None:
    import resource

    resource.setrlimit(resource.RLIMIT_AS    ,     (maximum_memory_bytes, maximum_memory_bytes) )
    resource.setrlimit(resource.RLIMIT_DATA    ,     (maximum_memory_bytes, maximum_memory_bytes) )
    if not platform.uname().system == "Darwin":
      resource.setrlimit(resource.RLIMIT_STACK    ,     (maximum_memory_bytes, maximum_memory_bytes) )

  faulthandler.disable()

  import builtins

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import os

  _UpperCamelCase           =    "1"

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import shutil

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import subprocess

  _UpperCamelCase           =    None  # type: ignore

  _UpperCamelCase           =    None

  import sys

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None




